Digital imaging is a process used to recognize objects of interest in an image by utilizing electronic sensors and advanced computing techniques with the aim of improving image quality parameters. Furthermore, digital imaging is intrinsically difficult due to the fact that image formation is basically a many-to-one-mapping (i.e. characterization of three-dimensional (3-d) objects can be deduced from either a single image or multiple images). In addition, several problems associated with low-contrast images, including: blurred images, noisy images, image conversion to digital form, transmission, handling, manipulation, and storage of large-volume images, have led to the development of efficient image processing and recognition algorithms. Specifically, digital imaging or computer vision involves image processing and pattern recognition techniques, whereby image processing techniques utilize image enhancement, manipulation, and analysis of images, and pattern recognition utilizes object identification from observed patterns and images. Recently, significant advances have been made in pattern recognition, through the use of several new types of computer architectures that utilize very large-scale integrated circuits (VLSI) and solid state memories with a variety of parallel high-speed computers, optical and opto-digital computers, as well as a variety of neural network architectures and implementations. Artificial neural networks (ANN) have shown to be highly capable in solving problems that are not governed by rules, or in which traditional techniques have failed or proved inadequate. The inherent parallel architecture and the fault tolerant nature of the artificial neural networks is maximally utilized to address problems in a variety of application areas related to the field of imaging. Artificial neural networks (ANN) find their application in pattern recognition (classification, clustering, feature selection), texture analysis, segmentation, image compression, color representation and several other aspects of image processing, with applications in medical imaging, remote sensing, aerospace, radars, and military applications.
Wavelets are a family of transforms whose basis functions are of short duration and finite energy. In contrast to the Fourier transform, which effectively assumes that a signal is stationary at time scales of interest, the wavelet transform determines a signal's frequency content as a function of time. Thus, the use of Fourier or wave transforms results in a trade-off between localization in the time and frequency domains. Specifically, the time and frequency domain analysis provided by the wavelet transform provides the opportunity to explore the nature of transient signals by representing the time varying spectral response through time-frequency maps, as well as to analyze signals for conditions where responses change significantly in amplitude during experiments. As such, wavelets have found application in situations that require analysis over a very short time duration or where information is localized.
Fractal geometry is the geometry of self-similarity in which objects appear to look similar at different scales. The key concept of fractal analysis relies on the fact that a fractal dimension can be considered as a quantitative measure of object surface heterogeneity because of its inherent self-similarity features. The fractal dimension can be interpreted as a measure of heterogeneity of a set of points on a plane, or in space, for instance, a measure of surface roughness.
Combining both techniques of wavelet and fractal analysis allows enhanced detection, discrimination, tracking and identification of objects to be achieved, however such techniques have yet to be utilized in imaging of polarimetric signals.
The physical algorithm and applied metrics of this study on the material characterization of space materials is shown in FIG. 1, with reference to polysilicon, a material found to exhibit higher diffused scattering characteristics, depolarization, and fractal dimension, with respect to amorphous silicon.
Polarimetric imaging of target objects/materials offers unique advantages for a wide range of detection and classification problems due to the intrinsic potential for high contrast in different polarization components of backscattered light that is detected from the target object/material during its imaging. Moreover, polarimetric imaging can yield high-specificity images of the target object/material in high-dynamic range and extreme condition scenarios, such as in scattering media, or cluttered environments, while at the same time acquiring information related to the material composition and the surface characteristics of the target object/material.
While polarimetric imaging provides the various advantages discussed above, there is still a need for further enhancement in target object/material detection, tracking, discrimination, and identification.
Therefore, there is a need for a system and method for polarimetric wavelet detection that is able to achieve enhanced detection of a target object/material, such as space materials. In addition, there is a need for a system and method for polarimetric wavelet detection that is able to remotely characterize a target object/material, such as space materials, with enhanced discrimination, localization, and high-dynamic range, and high sensitivity. Furthermore, there is a need for a system and method for polarimetric wavelet detection that combines cross-correlation and wavelet principles with polarimetric imaging at different aspect angles to achieve enhanced detection and characterization of a target object/material.